Furthermore, [1]} also consider heterogeneous data and convert estimated dependencies into path constraints on the data-generating causal model and encode them as a SAT instance. In another line of work, [2]} deal with learning causal models from multiple experiments- utilizing a Bayesian score to combine experimental and observational data. Finally, [3]} present a method for linear and non-Gaussian cases that allows clients access to different variables set and handles distributional shifts.
